This paper provides a framework in which multilevel Monte Carlo and continuous level Monte Carlo can be compared. In continuous level Monte Carlo the level of refinement is determined by an exponentially distributed random variable, which therefore heavily influences the computational complexity. We propose in this paper a variant of the algorithm, where the exponentially distributed random variable is generated by a quasi Monte Carlo sequence, resulting in a significant variance reduction. In the examples presented the quasi continuous level Monte Carlo algorithm outperforms multilevel and continuous level Monte Carlo by a clear margin.

翻译：本文构建了一个可比较多层蒙特卡罗方法与连续层级蒙特卡罗方法的统一框架。在连续层级蒙特卡罗方法中，细化层级由服从指数分布的随机变量决定，这对其计算复杂度产生了显著影响。本文提出了一种算法改进方案，即通过拟蒙特卡罗序列生成该指数分布随机变量，从而实现了显著的方差缩减。在本文呈现的算例中，准连续层级蒙特卡罗算法以明显优势优于多层蒙特卡罗方法与连续层级蒙特卡罗方法。